title: Semi-stochastic models for Salmonella infection within finishing pig units in the UK.
abstract: A multi-group semi-stochastic model is formulated to describe Salmonella dynamics on a pig herd within the UK and assess whether farm structure has any effect on the dynamics. The models include both direct transmission and indirect (via free-living infectious units in the environment and airborne infection). The basic reproduction number R0 is also investigated. The models estimate approximately 24.6% and 25.4% of pigs at slaughter weight will be infected with Salmonella within a slatted-floored and solid-floored unit respectively, which corresponds to values found in previous abattoir and farm studies, suggesting that the model has reasonable validity. Analysis of the models identified the shedding rate to be of particular importance in the control of Salmonella spread, a finding also evident in an increase in the R0 value.
authors.affiliation: Institute of Infection & Global Health, Department of Epidemiology & Population Health, University of Liverpool, Leahurst Campus, Chester High Road, Neston, Cheshire CH64 7TE, UK.
authors.forename: Alexander D C
authors.initials: AD
authors.lastname: Berriman
authors.forename: Damian
authors.initials: D
authors.lastname: Clancy
authors.forename: Helen E
authors.initials: HE
authors.lastname: Clough
authors.forename: Robert M
authors.initials: RM
authors.lastname: Christley
mesh_terms: D000003:Abattoirs; D000822:Animal Husbandry / Q000295:instrumentation; D000818:Animals; D050936:Basic Reproduction Number; D019295:Computational Biology; D008390:Markov Chains; D008954:Models, Biological; D012481:Salmonella Infections, Animal / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission*; D013269:Stochastic Processes; D034421:Sus scrofa; D013552:Swine; D013553:Swine Diseases / Q000453:epidemiology / Q000517:prevention & control / Q000635:transmission*; D006113:United Kingdom / Q000453:epidemiology
keywords: Basic reproduction number; R(0); Salmonella; Semi-stochastic model